Committing your changes

Learn how to commit changes you've made in your Dataform project

Committing changes

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Dataform uses the Git version control system to maintain a record of each change made to project files and to manage file versions. Each Dataform project has an associated Git repository.

When you first use Dataform we will automatically create a personal development branch for you called name_dev . Only you can see this branch and commit to it. All the changes you've made so far should be on this branch.

  1. Press the Commit Changes button:

    • In the modal you should see a list of all the changes you made to your project.
  2. Give your commits a description and press Commit .

  3. Click the Push to Master button to push your changes to the master branch:

    • The changes will now be on production and used for future schedule runs.

For more information on how version control works in Dataform, see our docs.


You’ve now created your two tables; order stats and customers . customers depends on order_stats and will not be run until order_stats is updated. You have added data quality tests so that you know the data is reliable and you have documented your datasets so other team members know what the tables contain. A schedule will run every hour to keep the tables up to date, and you will be automatically alerted if the pipeline fails or there are data quality issues.

Next Steps

Start working on your own project

Follow the steps in this tutorial to get started with your own project. Connect to your own warehouse and create your first dataset using SQL.

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What's next

Getting set up

Learn how to create a new BigQuery project and generate warehouse credentials.

Building your data model

Learn how to connect to a warehouse and create and publish your first dataset.

Managing dependencies

Learn how to use the ref function in Dataform and how to view your project in the Dependency tree.

Setting up a schedule

Learn how to set up a schedule and alerts in Dataform

Data quality tests and documenting datasets

Learn how to set up data quality tests using assertions and how to document your datasets